Matches in ScholarlyData for { ?s ?p ?o. }
- 85 creator kai-yu.
- 85 creator shenghuo-zhu.
- 85 creator xiaodan-song.
- 85 type InProceedings.
- 85 label "Learning Multiple Graphs for Document Recommendations".
- 85 sameAs 85.
- 85 abstract "The Web offers rich relational data with different semantics. In this paper, we address the problem of document recommendation in a digital library, where the documents in question are networked by citations and are associated with other entities by various relations. Due to the sparsity of a single graph and noise in graph construction, we propose a new method for combining multiple graphs to measure document similarities, where different factorization strategies are used based on the nature of different graphs. In particular, the new method seeks a single low-dimensional embedding of documents that captures their relative similarities in a latent space. Based on the obtained embedding, a new recommendation framework is developed using semi-supervised learning on graphs. In addition, we address the scalability issue and propose an incremental algorithm. The new incremental method significantly improves the efficiency by calculating the embedding for new incoming documents only. The new batch and incremental methods are evaluated on two real world datasets prepared from CiteSeer. Experiments demonstrate significant quality improvement for our batch method and significant efficiency improvement with tolerable quality loss for our incremental method.".
- 85 hasAuthorList authorList.
- 85 hasTopic World_Wide_Web.
- 85 isPartOf proceedings.
- 85 keyword "Collaborative Filtering".
- 85 keyword "Network Analysis".
- 85 keyword "Recommender Systems".
- 85 keyword "Semi-supervised Learning".
- 85 keyword "Spectral Clustering".
- 85 title "Learning Multiple Graphs for Document Recommendations".
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- item-1 hasContent ding-zhou.
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- item-2 hasContent shenghuo-zhu.
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- item-3 hasContent kai-yu.
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- item-4 hasContent xiaodan-song.
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- item-5 hasContent belle-tseng.
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- item-6 hasContent hongyuan-zha.
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- item-7 hasContent c-lee-giles.
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- 850 creator alvaro-bolivar.
- 850 creator wenyan-hu.
- 850 type InProceedings.
- 850 label "Online Auctions Efficiency: A Survey of eBay Auctions".
- 850 sameAs 850.
- 850 abstract "Online auctions have become a pervasive transaction mechanism for e-commerce. As the largest online marketplace in the world, eBay is an attractive case study that enables the study of online auctions utilizing data involving real people and transactions. In this paper, we present a detailed investigation and analysis of multiple online auction properties including: consumer surplus, sniping, bidding strategy and their cross-relationships. Our goal is to evaluate the theoretical foundations of auctions and discover patterns and behaviors hidden due to the lack of real and extensive transaction data. Among our findings, we uncover an important correlation among sniping and high surplus ratios, which implies the uncertainty of true value in a competitive environment. The key issue is the wrong assumption that bidder valuations are independent from each other, which leads to inefficient auctions. In order to address the inefficiencies of current online formats we introduce a declining price auction model customized for online transactions. Conceptually, this model ought to deal with the complexities of competition in an online environment while maximizing social welfare.".
- 850 hasAuthorList authorList.
- 850 hasTopic World_Wide_Web.
- 850 isPartOf proceedings.
- 850 keyword "Auction Theory".
- 850 keyword "Auctions Efficiency".
- 850 keyword "Online Auctions".
- 850 title "Online Auctions Efficiency: A Survey of eBay Auctions".
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- item-1 hasContent wenyan-hu.
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- item-2 hasContent alvaro-bolivar.
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- 86 creator c-lee-giles.
- 86 creator ding-zhou.
- 86 creator hongyuan-zha.
- 86 creator jiang-bian.
- 86 creator shuyi-zheng.
- 86 type InProceedings.
- 86 label "Exploring Social Annotations for Information Retrieval".
- 86 sameAs 86.
- 86 abstract "Recently, social annotation has been gaining increasing popularity in many Web-based applications, leading to an emerging research area in text analysis and information retrieval. This paper is concerned with developing probabilistic models and computational algorithms for social annotations. We propose a unified framework to combine the modeling of social annotations with the language modeling-based methods for information retrieval. The proposed approach consists of two steps: (1) we seek to discover topics in the contents and annotations of documents while categorizing the users by domains; and (2) we enhance document and query language models by incorporating user domain interests as well as topical background models. Differences in user domain expertise are also considered when combining the discovered user domain interests. In particular, we propose a new general generative model for social annotations, which is then simplified to a computationally trackable hierarchical Bayesian network. Then we apply smoothing techniques in a risk minimization framework to incorporate the topical information to language models. Experiments are carried out on a real-world annotation data set sampled from del.icio.us. Our results demonstrate significant improvements over the alternative approaches without consideration of topical information, social annotations, user expertise, or simple incorporation of topic analysis.".
- 86 hasAuthorList authorList.
- 86 hasTopic World_Wide_Web.
- 86 isPartOf proceedings.
- 86 keyword "folksonomy".
- 86 keyword "information retrieval".
- 86 keyword "language modeling".
- 86 keyword "social annotations".
- 86 title "Exploring Social Annotations for Information Retrieval".
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